{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c683eb4f",
   "metadata": {},
   "source": [
    "## 集成预测器\n",
    "集成模型是用集成学习的思想去把多个PaddleTS的预测器集合成一个预测器。目前我们支持两种集成预测器StackingEnsembleForecaster 和 WeightingEnsembleForecaster\n",
    "\n",
    "步骤包括：\n",
    "\n",
    "1.准备数据\n",
    "\n",
    "2.准备模型\n",
    "\n",
    "3.组装和拟合模型\n",
    "\n",
    "4.进行回测\n",
    "\n",
    "5.模型保存与加载"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60ad2eb8",
   "metadata": {},
   "source": [
    "### 1.准备数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1c09f777",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#获取PaddleTS内置数据集\n",
    "from paddlets.datasets.repository import get_dataset\n",
    "tsdataset = get_dataset(\"WTH\")\n",
    "\n",
    "#分割数据成训练集/测试集/验证集\n",
    "ts_train, ts_val_test = tsdataset.split(\"2012-03-31 23:00:00\")\n",
    "ts_val, ts_test = ts_val_test.split(\"2013-02-28 23:00:00\")\n",
    "ts_train.plot(add_data=[ts_val, ts_test], labels=['Val', 'Test'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "da47c163",
   "metadata": {},
   "outputs": [],
   "source": [
    "#用StandardScaler去归一化数据（可选）\n",
    "from paddlets.transform.sklearn_transforms import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "scaler.fit(ts_train)\n",
    "ts_train = scaler.transform(ts_train)\n",
    "ts_val = scaler.transform(ts_val)\n",
    "ts_test = scaler.transform(ts_test)\n",
    "ts_val_test = scaler.transform(ts_val_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "444ff620",
   "metadata": {},
   "source": [
    "### 2. 准备模型\n",
    "准备集成预测器需要的底层模型\n",
    "\n",
    "请注意，为了保持模型预测的一致性， <font color=red>in_chunk_len</font>,  <font color=red>out_chunk_len</font>,  <font color=red>skip_chunk_len</font>, 这三个参数已经被提取到集成模型中，您可以在base模型的参数中忽略这三个参数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "53947313",
   "metadata": {},
   "outputs": [],
   "source": [
    "from paddlets.models.forecasting import MLPRegressor\n",
    "from paddlets.models.forecasting import NHiTSModel\n",
    "from paddlets.models.forecasting import RNNBlockRegressor\n",
    "\n",
    "\n",
    "\n",
    "nhits_params = {\n",
    "    'sampling_stride': 24,\n",
    "    'eval_metrics':[\"mse\", \"mae\"],\n",
    "    'batch_size': 32,\n",
    "    'max_epochs': 10,\n",
    "    'patience': 100\n",
    "}\n",
    "rnn_params = {\n",
    "    'sampling_stride': 24,\n",
    "    'eval_metrics': [\"mse\", \"mae\"],\n",
    "    'batch_size': 32,\n",
    "    'max_epochs': 10,\n",
    "    'patience': 100,\n",
    "}\n",
    "mlp_params = {\n",
    "    'sampling_stride': 24,\n",
    "    'eval_metrics': [\"mse\", \"mae\"],\n",
    "    'batch_size': 32,\n",
    "    'max_epochs': 10,\n",
    "    'patience': 100,\n",
    "    'use_bn': True,\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ba90e59",
   "metadata": {},
   "source": [
    "### 3. 组装和拟合模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6885a482",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/qiguoning01/Desktop/Erised/baidu/new_bts1/baidu/erised/bts/bts/models/representation/dl/cost.py:217: DeprecationWarning: invalid escape sequence \\[\n",
      "  \"\"\"\n",
      "/Users/qiguoning01/Desktop/Erised/baidu/new_bts1/baidu/erised/bts/bts/models/representation/dl/ts2vec.py:122: DeprecationWarning: invalid escape sequence \\[\n",
      "  \"\"\"\n",
      "[2022-11-02 19:41:06,391] [bts.models.common.callbacks.callbacks] [INFO] epoch 000| loss: 2.044766| val_0_mse: 0.627053| val_0_mae: 0.615734| 0:00:02s\n",
      "[2022-11-02 19:41:08,549] [bts.models.common.callbacks.callbacks] [INFO] epoch 001| loss: 0.995072| val_0_mse: 0.477169| val_0_mae: 0.541606| 0:00:04s\n",
      "[2022-11-02 19:41:10,678] [bts.models.common.callbacks.callbacks] [INFO] epoch 002| loss: 0.724469| val_0_mse: 0.393254| val_0_mae: 0.483179| 0:00:06s\n",
      "[2022-11-02 19:41:12,862] [bts.models.common.callbacks.callbacks] [INFO] epoch 003| loss: 0.579931| val_0_mse: 0.354661| val_0_mae: 0.458536| 0:00:08s\n",
      "[2022-11-02 19:41:15,002] [bts.models.common.callbacks.callbacks] [INFO] epoch 004| loss: 0.508339| val_0_mse: 0.328303| val_0_mae: 0.439573| 0:00:10s\n",
      "[2022-11-02 19:41:17,217] [bts.models.common.callbacks.callbacks] [INFO] epoch 005| loss: 0.447965| val_0_mse: 0.310276| val_0_mae: 0.428114| 0:00:13s\n",
      "[2022-11-02 19:41:19,344] [bts.models.common.callbacks.callbacks] [INFO] epoch 006| loss: 0.382195| val_0_mse: 0.288194| val_0_mae: 0.411304| 0:00:15s\n",
      "[2022-11-02 19:41:21,553] [bts.models.common.callbacks.callbacks] [INFO] epoch 007| loss: 0.338846| val_0_mse: 0.276100| val_0_mae: 0.398817| 0:00:17s\n",
      "[2022-11-02 19:41:23,768] [bts.models.common.callbacks.callbacks] [INFO] epoch 008| loss: 0.318228| val_0_mse: 0.253852| val_0_mae: 0.382713| 0:00:19s\n",
      "[2022-11-02 19:41:25,918] [bts.models.common.callbacks.callbacks] [INFO] epoch 009| loss: 0.284756| val_0_mse: 0.245020| val_0_mae: 0.375762| 0:00:21s\n",
      "[2022-11-02 19:41:25,929] [bts.models.common.callbacks.callbacks] [INFO] Stop training because you reached max_epochs = 10 with best_epoch = 9 and best_val_0_mae = 0.375762\n",
      "[2022-11-02 19:41:25,931] [bts.models.common.callbacks.callbacks] [INFO] Best weights from best epoch are automatically used!\n",
      "[2022-11-02 19:41:25,963] [bts.utils.utils] [WARNING] error occurred while import autots, err: No module named 'ray'\n",
      "[2022-11-02 19:41:34,397] [bts.models.common.callbacks.callbacks] [INFO] epoch 000| loss: 0.869002| val_0_mse: 0.800661| val_0_mae: 0.719128| 0:00:00s\n",
      "[2022-11-02 19:41:35,081] [bts.models.common.callbacks.callbacks] [INFO] epoch 001| loss: 0.530023| val_0_mse: 0.413881| val_0_mae: 0.493660| 0:00:01s\n",
      "[2022-11-02 19:41:35,768] [bts.models.common.callbacks.callbacks] [INFO] epoch 002| loss: 0.263927| val_0_mse: 0.221361| val_0_mae: 0.357488| 0:00:02s\n",
      "[2022-11-02 19:41:36,449] [bts.models.common.callbacks.callbacks] [INFO] epoch 003| loss: 0.170616| val_0_mse: 0.167941| val_0_mae: 0.307197| 0:00:02s\n",
      "[2022-11-02 19:41:37,149] [bts.models.common.callbacks.callbacks] [INFO] epoch 004| loss: 0.138635| val_0_mse: 0.142683| val_0_mae: 0.279215| 0:00:03s\n",
      "[2022-11-02 19:41:37,837] [bts.models.common.callbacks.callbacks] [INFO] epoch 005| loss: 0.123357| val_0_mse: 0.128654| val_0_mae: 0.262584| 0:00:04s\n",
      "[2022-11-02 19:41:38,550] [bts.models.common.callbacks.callbacks] [INFO] epoch 006| loss: 0.112267| val_0_mse: 0.121767| val_0_mae: 0.251373| 0:00:04s\n",
      "[2022-11-02 19:41:39,277] [bts.models.common.callbacks.callbacks] [INFO] epoch 007| loss: 0.105226| val_0_mse: 0.114018| val_0_mae: 0.241401| 0:00:05s\n",
      "[2022-11-02 19:41:39,961] [bts.models.common.callbacks.callbacks] [INFO] epoch 008| loss: 0.100363| val_0_mse: 0.109506| val_0_mae: 0.234982| 0:00:06s\n",
      "[2022-11-02 19:41:40,646] [bts.models.common.callbacks.callbacks] [INFO] epoch 009| loss: 0.096804| val_0_mse: 0.106421| val_0_mae: 0.230101| 0:00:06s\n",
      "[2022-11-02 19:41:40,647] [bts.models.common.callbacks.callbacks] [INFO] Stop training because you reached max_epochs = 10 with best_epoch = 9 and best_val_0_mae = 0.230101\n",
      "[2022-11-02 19:41:40,648] [bts.models.common.callbacks.callbacks] [INFO] Best weights from best epoch are automatically used!\n",
      "[2022-11-02 19:41:40,663] [bts.utils.utils] [WARNING] error occurred while import autots, err: No module named 'ray'\n",
      "/Users/qiguoning01/opt/anaconda3/envs/python37/lib/python3.7/site-packages/paddle/nn/layer/norm.py:654: UserWarning: When training, we now always track global mean and variance.\n",
      "  \"When training, we now always track global mean and variance.\")\n",
      "[2022-11-02 19:41:44,890] [bts.models.common.callbacks.callbacks] [INFO] epoch 000| loss: 0.453417| val_0_mse: 0.270509| val_0_mae: 0.393524| 0:00:00s\n",
      "/Users/qiguoning01/opt/anaconda3/envs/python37/lib/python3.7/site-packages/paddle/nn/layer/norm.py:654: UserWarning: When training, we now always track global mean and variance.\n",
      "  \"When training, we now always track global mean and variance.\")\n",
      "[2022-11-02 19:41:44,933] [bts.models.common.callbacks.callbacks] [INFO] epoch 001| loss: 0.210326| val_0_mse: 0.200631| val_0_mae: 0.324325| 0:00:00s\n",
      "/Users/qiguoning01/opt/anaconda3/envs/python37/lib/python3.7/site-packages/paddle/nn/layer/norm.py:654: UserWarning: When training, we now always track global mean and variance.\n",
      "  \"When training, we now always track global mean and variance.\")\n",
      "[2022-11-02 19:41:44,974] [bts.models.common.callbacks.callbacks] [INFO] epoch 002| loss: 0.181472| val_0_mse: 0.182237| val_0_mae: 0.307475| 0:00:00s\n",
      "/Users/qiguoning01/opt/anaconda3/envs/python37/lib/python3.7/site-packages/paddle/nn/layer/norm.py:654: UserWarning: When training, we now always track global mean and variance.\n",
      "  \"When training, we now always track global mean and variance.\")\n",
      "[2022-11-02 19:41:45,011] [bts.models.common.callbacks.callbacks] [INFO] epoch 003| loss: 0.162976| val_0_mse: 0.162275| val_0_mae: 0.291696| 0:00:00s\n",
      "/Users/qiguoning01/opt/anaconda3/envs/python37/lib/python3.7/site-packages/paddle/nn/layer/norm.py:654: UserWarning: When training, we now always track global mean and variance.\n",
      "  \"When training, we now always track global mean and variance.\")\n",
      "[2022-11-02 19:41:45,048] [bts.models.common.callbacks.callbacks] [INFO] epoch 004| loss: 0.147350| val_0_mse: 0.151771| val_0_mae: 0.278754| 0:00:00s\n",
      "/Users/qiguoning01/opt/anaconda3/envs/python37/lib/python3.7/site-packages/paddle/nn/layer/norm.py:654: UserWarning: When training, we now always track global mean and variance.\n",
      "  \"When training, we now always track global mean and variance.\")\n",
      "[2022-11-02 19:41:45,086] [bts.models.common.callbacks.callbacks] [INFO] epoch 005| loss: 0.137939| val_0_mse: 0.141597| val_0_mae: 0.264598| 0:00:00s\n",
      "/Users/qiguoning01/opt/anaconda3/envs/python37/lib/python3.7/site-packages/paddle/nn/layer/norm.py:654: UserWarning: When training, we now always track global mean and variance.\n",
      "  \"When training, we now always track global mean and variance.\")\n",
      "[2022-11-02 19:41:45,124] [bts.models.common.callbacks.callbacks] [INFO] epoch 006| loss: 0.128452| val_0_mse: 0.136349| val_0_mae: 0.258482| 0:00:00s\n",
      "/Users/qiguoning01/opt/anaconda3/envs/python37/lib/python3.7/site-packages/paddle/nn/layer/norm.py:654: UserWarning: When training, we now always track global mean and variance.\n",
      "  \"When training, we now always track global mean and variance.\")\n",
      "[2022-11-02 19:41:45,163] [bts.models.common.callbacks.callbacks] [INFO] epoch 007| loss: 0.124158| val_0_mse: 0.130048| val_0_mae: 0.253282| 0:00:00s\n",
      "/Users/qiguoning01/opt/anaconda3/envs/python37/lib/python3.7/site-packages/paddle/nn/layer/norm.py:654: UserWarning: When training, we now always track global mean and variance.\n",
      "  \"When training, we now always track global mean and variance.\")\n",
      "[2022-11-02 19:41:45,206] [bts.models.common.callbacks.callbacks] [INFO] epoch 008| loss: 0.121132| val_0_mse: 0.128601| val_0_mae: 0.248995| 0:00:00s\n",
      "/Users/qiguoning01/opt/anaconda3/envs/python37/lib/python3.7/site-packages/paddle/nn/layer/norm.py:654: UserWarning: When training, we now always track global mean and variance.\n",
      "  \"When training, we now always track global mean and variance.\")\n",
      "[2022-11-02 19:41:45,248] [bts.models.common.callbacks.callbacks] [INFO] epoch 009| loss: 0.118462| val_0_mse: 0.127497| val_0_mae: 0.248993| 0:00:00s\n",
      "[2022-11-02 19:41:45,249] [bts.models.common.callbacks.callbacks] [INFO] Stop training because you reached max_epochs = 10 with best_epoch = 9 and best_val_0_mae = 0.248993\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2022-11-02 19:41:45,249] [bts.models.common.callbacks.callbacks] [INFO] Best weights from best epoch are automatically used!\n",
      "[2022-11-02 19:41:45,263] [bts.utils.utils] [WARNING] error occurred while import autots, err: No module named 'ray'\n"
     ]
    }
   ],
   "source": [
    "from paddlets.ensemble import StackingEnsembleForecaster\n",
    "\n",
    "reg = StackingEnsembleForecaster(\n",
    "in_chunk_len=7 * 24,\n",
    "out_chunk_len=24,\n",
    "skip_chunk_len=0,\n",
    "estimators=[(NHiTSModel, nhits_params),(RNNBlockRegressor, rnn_params), (MLPRegressor, mlp_params)])\n",
    "\n",
    "reg.fit(ts_train, ts_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "511665c3",
   "metadata": {},
   "source": [
    "### 4. 进行回测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a840d014",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2022-11-02 19:44:08,506] [bts.utils.backtest] [INFO] Parameter 'predict_window' not set, default set to model.out_chunk_len 24\n",
      "[2022-11-02 19:44:08,509] [bts.utils.backtest] [INFO] Parameter 'stride' not set, default set to predict_window 24\n",
      "[2022-11-02 19:44:08,519] [bts.utils.utils] [WARNING] error occurred while import autots, err: No module named 'ray'\n",
      "Backtest Progress: 100%|████████████████████████████████████████████████████| 306/306 [00:12<00:00, 25.50it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from paddlets.utils import backtest\n",
    "from paddlets.metrics import MAE\n",
    "mae, ts_pred = backtest(data=ts_val_test,\n",
    "                model=reg,\n",
    "                start=\"2013-03-01 00:00:00\", \n",
    "                metric=MAE(),\n",
    "                return_predicts=True\n",
    "                )\n",
    "\n",
    "ts_val_test.plot(add_data=ts_pred,labels=\"backtest\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "701ae5c8",
   "metadata": {},
   "source": [
    "### 5.模型保存与加载"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "43003ef5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#保存模型\n",
    "reg.save(path=\"/tmp/ensemble_test1/\")\n",
    "\n",
    "\n",
    "#加载模型\n",
    "from paddlets.ensemble import StackingEnsembleForecaster\n",
    "reg = StackingEnsembleForecaster.load(path=\"/tmp/ensemble_test1/\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
